基于多式传感器信号的混合现实人机交互任务的认知负载分类
Yukang Hou1, Qingsheng Xie1, Ning Zhang1
1Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education, Guizhou University, Guiyang, 550025, China.
Scientific reports
|April 21, 2025
概括
这项研究开发了一种系统,用于监测混合现实 (MR) 环境中的认知负载. 该系统准确地检测高认知负载,提高了MR工作的安全性和性能.
科学领域:
- 人与计算机的交互 (HCI)
- 认知科学 认知科学
- 可穿戴技术可穿戴技术
背景情况:
- 在混合现实 (MR) 中评估认知负载对于人机交互 (HCI) 至关重要.
- 目前用于评估MR认知负载的方法有限.
- 高度的认知负载会对用户的性能和安全产生负面影响.
研究的目的:
- 建立一个MR多模式实验平台,以诱导和测量不同的认知负载水平.
- 确定有效的传感器数据流和算法,用于MR的认知负载分类.
- 设计和验证一个MR数字双胞胎工厂系统,用于实时认知负载警告.
主要方法:
- 开发了一个MR实验平台,有三个环境来操纵认知负载.
- 采集了使用HoloLens 2和可穿戴心率传感器的生理和设备数据.
- 通过NASA-TLX评估认知负载,并使用改进的变压器-CL算法分析数据.
主要成果:
- 在高认知负载下,操作时间增加了49%.
- 高认知负载与增加的焦虑,丧和性能下降相关.
- 开发的MR系统在分类认知负载方面实现了95.83%的准确性.
结论:
- 该MR多式联络平台有效地诱导和测量认知负载.
- 变压器-CL算法和传感器数据适用于认知负载分类.
- MR 数字双胞胎工厂系统可以有效地警告用户高认知负载,提高安全性和性能.
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